Harnessing Deep Learning for Precision Diagnosis of Pancreatic Cancer

Abstract

Introduction:

The pancreatic cancer cells are developed due to over or high cell growth , these can be also caused by genetic mutation, habits of daily life and medical conditions. Early detection of the pancreatic cancer can be challenging ,treatment often involves chemotherapy ,immune therapy and drugs related to stage of cancer . these drugs often develop side effects .AI driven methods like Random forest , Naïve bayes and CNN , assist with medical professionals in diagnosing pancreatic cancer  with the help of Medicals imaging and lab test analysis .preventive measures like maintaining good BMI and dietary changes are some of measures to be taken to ensure low risk of pancreatic cancer .

Objectives:

The deep learning and machine learning can be used for the accurate and early diagnosis of the pancreatic cancer , improving detection rate and patient outcomes. The AI models analyse medical images, lab results and genetic data ,to classify different types and stages of cancer for a perfect personalised treatment strategy .

Methologies:

the AI based method combines machine learning and deep learning for the detection and classification of pancreatic cancer accurately. The machine learning algorithms like random forest and naïve bayes analyse clincal and genetic test result data. The deep learning algorithms like CNN use medical imaging for the detection of tumor in the image.

Results:

The machine learning algorithm is used when we use the lab data, in which we add data to ml model which then predicts whether the data has pancreatic cancer or not, also classifies which type of pancreatic cancer. Where the Dl model uses  CT images which automatically detects tumor in images .

Conclusion:

the model enhances medical diagnosis and advanced health care technologies. the integrated approach improves the accuracy, with coordination evolving health care standards . this demonstrates the potential of AI and data integration in addressing complex medical challenges .